mlsanityData to 7 Oct 2026

Spatio-Temporal Graph Neural Networks: A Survey

arXiv 2301.10569 ↗First posted 25 Jan 2023cs.LGcited by 23

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Abstract (arXiv)

Graph Neural Networks have gained huge interest in the past few years. These powerful algorithms expanded deep learning models to non-Euclidean space and were able to achieve state of art performance in various applications including recommender systems and social networks. However, this performance is based on static graph structures assumption which limits the Graph Neural Networks performance when the data varies with time. Spatiotemporal Graph Neural Networks are extension of Graph Neural Networks that takes the time factor into account. Recently, various Spatiotemporal Graph Neural Network algorithms were proposed and achieved superior performance compared to other deep learning algorithms in several time dependent applications. This survey discusses interesting topics related to Spatiotemporal Graph Neural Networks, including algorithms, applications, and open challenges.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 25 Jan 2023.

Builds on

25 of 36 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  6. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  7. 2019Graph Transformer Networkscited by 520
  8. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  9. 2021TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Trackingcited by 93
  10. 2021Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attentioncited by 86
  11. 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
  12. 2022TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphscited by 59
13 more
  1. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  2. 2022TREND: TempoRal Event and Node Dynamics for Graph Representation Learningcited by 50
  3. 2019STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaitscited by 36
  4. 2020FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecastingcited by 30
  5. 2021Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Networkcited by 26
  6. 2021Spatio-temporal graph neural networks for multi-site PV power forecastingcited by 22
  7. 2019Recurrent Space-time Graph Neural Networkscited by 19
  8. 2022Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attentioncited by 15
  9. 2021STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networkscited by 14
  10. 2019Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Datacited by 11
  11. 2021Space-Time Graph Neural Networkscited by 10
  12. 2021Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Networkcited by 8
  13. 2022Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecastingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 8; 2025: 5; 2026: 6.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  4. 2024A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directionscited by 13
  5. 2024Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matchingcited by 8
  6. 2025A Survey of Link Prediction in Temporal Networkscited by 6
  7. 2024STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecastingcited by 4
  8. 2024Improving Graph Neural Networks by Learning Continuous Edge Directionscited by 3
  9. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  10. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  11. 2025Urban Computing in the Era of Large Language Modelscited by 2
  12. 2025Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attentioncited by 2
11 more
  1. 2026Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Modelscited by 2
  2. 2023Multi-Temporal Relationship Inference in Urban Areascited by 1
  3. 2024Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classificationcited by 1
  4. 2024Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Datacited by 1
  5. 2025FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagationcited by 1
  6. 2026FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecastingcited by 1
  7. 2026Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learningcited by 1
  8. 2025Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNscited by 0
  9. 2026Efficient Prompt Learning for Traffic Forecastingcited by 0
  10. 2026Graph Mamba Operator: A Latent Simulator for Interacting Particle Systemscited by 0
  11. 2026Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detectioncited by 0